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--- |
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tags: |
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- audioseal |
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inference: false |
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--- |
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# AudioSeal |
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We introduce AudioSeal, a method for speech localized watermarking, with state-of-the-art robustness and detector speed. It jointly trains a generator that embeds a watermark in the audio, and a detector that detects the watermarked fragments in longer audios, even in the presence of editing. |
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Audioseal achieves state-of-the-art detection performance of both natural and synthetic speech at the sample level (1/16k second resolution), it generates limited alteration of signal quality and is robust to many types of audio editing. |
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Audioseal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed — achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications. |
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# 🧉 Installation |
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AudioSeal requires Python >=3.8, Pytorch >= 1.13.0, [omegaconf](https://omegaconf.readthedocs.io/), [julius](https://pypi.org/project/julius/), and numpy. To install from PyPI: |
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``` |
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pip install audioseal |
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``` |
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To install from source: Clone this repo and install in editable mode: |
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``` |
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git clone https://github.com/facebookresearch/audioseal |
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cd audioseal |
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pip install -e . |
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``` |
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# ⚙️ Models |
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We provide the checkpoints for the following models: |
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- AudioSeal Generator. |
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It takes as input an audio signal (as a waveform), and outputs a watermark of the same size as the input, that can be added to the input to watermark it. |
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Optionally, it can also take as input a secret message of 16-bits that will be encoded in the watermark. |
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- AudioSeal Detector. |
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It takes as input an audio signal (as a waveform), and outputs a probability that the input contains a watermark at each sample of the audio (every 1/16k s). |
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Optionally, it may also output the secret message encoded in the watermark. |
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Note that the message is optional and has no influence on the detection output. It may be used to identify a model version for instance (up to $2**16=65536$ possible choices). |
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**Note**: We are working to release the training code for anyone wants to build their own watermarker. Stay tuned ! |
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# 🧮 Usage |
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Audioseal provides a simple API to watermark and detect the watermarks from an audio sample. Example usage: |
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```python |
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from audioseal import AudioSeal |
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# model name corresponds to the YAML card file name found in audioseal/cards |
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model = AudioSeal.load_generator("audioseal_wm_16bits") |
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# Other way is to load directly from the checkpoint |
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# model = Watermarker.from_pretrained(checkpoint_path, device = wav.device) |
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# a torch tensor of shape (batch, channels, samples) and a sample rate |
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# It is important to process the audio to the same sample rate as the model |
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# expectes. In our case, we support 16khz audio |
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wav, sr = ..., 16000 |
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watermark = model.get_watermark(wav, sr) |
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# Optional: you can add a 16-bit message to embed in the watermark |
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# msg = torch.randint(0, 2, (wav.shape(0), model.msg_processor.nbits), device=wav.device) |
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# watermark = model.get_watermark(wav, message = msg) |
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watermarked_audio = wav + watermark |
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detector = AudioSeal.load_detector("audioseal_detector_16bits") |
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# To detect the messages in the high-level. |
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result, message = detector.detect_watermark(watermarked_audio, sr) |
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print(result) # result is a float number indicating the probability of the audio being watermarked, |
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print(message) # message is a binary vector of 16 bits |
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# To detect the messages in the low-level. |
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result, message = detector(watermarked_audio, sr) |
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# result is a tensor of size batch x 2 x frames, indicating the probability (positive and negative) of watermarking for each frame |
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# A watermarked audio should have result[:, 1, :] > 0.5 |
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print(result[:, 1 , :]) |
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# Message is a tensor of size batch x 16, indicating of the probability of each bit to be 1. |
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# message will be a random tensor if the detector detects no watermarking from the audio |
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print(message) |
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``` |
